Mahir Musleh
July 30, 2019
-Take a look how some Canadian provinces’ population changed overtime
-Take a look at some regression about what affects the Canadian GNI per capita
Codes for interactive map generation
prov.pop <- fread("./can_pop.csv",stringsAsFactors = FALSE)
colnames(prov.pop)[1] <- "Province"
prov.pop[] <- lapply(prov.pop, function(x) gsub(",","",x))
prov.pop[] <- lapply(prov.pop, function(x) as.character(x))
prov.pop[,2:272] <- lapply(prov.pop[,2:272], function(x) as.numeric(x))
region <- readOGR("./src/ref/ne_50m_admin_1_states_provinces_lakes", encoding='UTF-8')
prov.pop %>% leaflet() %>%
addTiles() %>%
setView(-100, 62, zoom = 3) %>%
addPolygons(data = subset(region, name %in% c("Quebec","British Columbia", "Alberta", "Saskatchewan", "Manitoba", "Ontario", "Quebec", "New Brunswick", "Prince Edward Island", "Nova Scotia", "Newfoundland and Labrador", "Yukon", "Northwest Territories", "Nunavut")),
fillColor = topo.colors(15, alpha = NULL),
weight = 1) %>%
addCircles(popup = paste0(prov.pop$Province),
weight = 4,
radius = (prov.pop$`1952 Q1`)*.015,color = "red")The shiny app will have a slider to change the quarter of the year and the population change, represented by the size of the circle, will change.

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Data: World Bank Databank
Values were converted into logarithm form first
Summary
Call:
lm(formula = GNI ~ . - Year, data = indicator.1)
Residuals:
Min 1Q Median 3Q Max
-0.081389 -0.017057 0.001302 0.024043 0.072976
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -10.41019 0.37103 -28.058 < 2e-16 ***
EXP -0.38919 0.09553 -4.074 0.000195 ***
IMP 0.54996 0.12463 4.413 6.75e-05 ***
CONS -0.22028 0.21774 -1.012 0.317356
GEXP 0.65077 0.17751 3.666 0.000673 ***
INV 0.20923 0.05560 3.763 0.000503 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03616 on 43 degrees of freedom
Multiple R-squared: 0.9976, Adjusted R-squared: 0.9973
F-statistic: 3562 on 5 and 43 DF, p-value: < 2.2e-16
Plot